Papers
Viewing 1-10 of 380 papers
What Does My QA Model Know? Devising Controlled Probes using Expert Knowledge
Kyle Richardson, Ashish SabharwalTACL • 2020Open-domain question answering (QA) is known to involve several underlying knowledge and reasoning challenges, but are models actually learning such knowledge when trained on benchmark tasks? To investigate this, we introduce several new challenge tasks that probe whether state-of-theart QA models… moreText Modular Networks: Learning to Decompose Tasks in the Language of Existing Models
Tushar Khot, Daniel Khashabi, Kyle Richardson, Peter Clark, Ashish SabharwalarXiv • 2020A common approach to solve complex tasks is by breaking them down into simple sub-problems that can then be solved by simpler modules. However, these approaches often need to be designed and trained specifically for each complex task. We propose a general approach, Text Modular Networks(TMNs… moreAllenAct: A Framework for Embodied AI Research
Luca Weihs, J. Salvador, Klemen Kotar, Unnat Jain, Kuo-Hao Zeng, R. Mottaghi, Aniruddha Kembhavi arXiv • 2020The domain of Embodied AI, in which agents learn to complete tasks through interaction with their environment from egocentric observations, has experienced substantial growth with the advent of deep reinforcement learning and increased interest from the computer vision, NLP, and robotics… moreA Cordial Sync: Going Beyond Marginal Policies for Multi-Agent Embodied Tasks
Unnat Jain, Luca Weihs, Eric Kolve, Ali Farhadi, Svetlana Lazebnik, Aniruddha Kembhavi, Alexander SchwingECCV • 2020Autonomous agents must learn to collaborate. It is not scalable to develop a new centralized agent every time a task’s difficulty outpaces a single agent’s abilities. While multi-agent collaboration research has flourished in gridworld-like environments, relatively little work has considered… moreGrounded Situation Recognition
Sarah Pratt, Mark Yatskar, Luca Weihs, Ali Farhadi, Aniruddha KembhaviECCV • 2020We introduce Grounded Situation Recognition (GSR), a task that requires producing structured semantic summaries of images describing: the primary activity, entities engaged in the activity with their roles (e.g. agent, tool), and bounding-box groundings of entities. GSR presents important technical… moreSpatially Aware Multimodal Transformers for TextVQA
Yash Kant, Dhruv Batra, Peter Anderson, Alex Schwing, Devi Parikh, Jiasen Lu, Harsh AgrawalECCV • 2020Textual cues are essential for everyday tasks like buying groceries and using public transport. To develop this assistive technology, we study the TextVQA task, i.e., reasoning about text in images to answer a question. Existing approaches are limited in their use of spatial relations and rely on… moreVisualCOMET: Reasoning about the Dynamic Context of a Still Image
Jae Sung Park, Chandra Bhagavatula, Roozbeh Mottaghi, Ali Farhadi, Yejin ChoiECCV • 2020Even from a single frame of a still image, people can reason about the dynamic story of the image before, after, and beyond the frame. For example, given an image of a man struggling to stay afloat in water, we can reason that the man fell into the water sometime in the past, the intent of that man… moreoLMpics - On what Language Model Pre-training Captures
Alon Talmor, Yanai Elazar, Yoav Goldberg, Jonathan BerantTACL • 2020Recent success of pre-trained language models (LMs) has spurred widespread interest in the language capabilities that they possess. However, efforts to understand whether LM representations are useful for symbolic reasoning tasks have been limited and scattered. In this work, we propose eight… moreBreak It Down: A Question Understanding Benchmark
Tomer Wolfson, Mor Geva, Ankit Gupta, Matt Gardner, Yoav Goldberg, Daniel Deutch, Jonathan BerantTACL • 2020Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer. In this work, we introduce a Question Decomposition Meaning Representation (QDMR) for questions. QDMR constitutes the ordered list of steps, expressed through… moreAdversarial Filters of Dataset Biases
Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula, Rowan Zellers, Matthew E. Peters, Ashish Sabharwal, Yejin Choi ICML • 2020Large neural models have demonstrated humanlevel performance on language and vision benchmarks such as ImageNet and Stanford Natural Language Inference (SNLI). Yet, their performance degrades considerably when tested on adversarial or out-of-distribution samples. This raises the question of whether… more